Biological productivity in the summer Vietnam boundary upwelling
system in the western South China Sea, as in many coastal upwelling systems,
is strongly modulated by wind. However, the role of ocean circulation and
mesoscale eddies has not been elucidated. Here, we show a close
spatiotemporal covariability between primary production and kinetic energy.
High productivity is associated with high kinetic energy, which accounts for
∼15 % of the production variability. Results from a
physical–biological coupled model reveal that the elevated kinetic energy is
linked to the strength of the current separation from the coast. In the low
production scenario, the circulation is not only weaker but also shows weak
separation. In the higher production case, the separated current forms an
eastward jet into the interior South China Sea, and the associated southern
recirculation traps nutrients and favors productivity. When separation is
absent, the model shows weakened circulation and eddy activity, with
∼21 % less nitrate inventory and ∼16 %
weaker primary productivity.

The South China Sea (SCS) is a large semi-enclosed marginal sea located in
the western Pacific Ocean (Fig. 1a). It is bordered
by extensive continental shelves along the southern coast of China and
northeastern Vietnam, and the Sunda Shelf south of Vietnam (Fig. 1). It has a
deep interior basin which can be as deep as 5000 m (Liu et al., 2010; Wong et al., 2007). The SCS is
predominantly controlled by the East Asian Monsoon. The wind is
southwesterly from June to September and northeasterly from November to
March (Liu et al., 2002). Because of efficient biological production, the
interior SCS has a low nutrient concentration in the euphotic zone,
displaying an oligotrophic condition (Wong et al.,
2007).

Coastal upwelling is one of the most important processes for ocean
productivity and fisheries (Bakun, 1996; Cushing, 1969; Mittelstaedt,
1986). During southwesterly monsoon, upwelling-favorable wind prevails along
the southern coast of Vietnam over the complex topography
(Fig. 1b). The offshore Ekman transport drives
surface divergence and results in coastal upwelling of cold and
nutrient-rich subsurface water. We refer to this region of interest as the
Vietnam boundary upwelling system (VBUS). The VBUS is centered near
∼109∘ E between 14 and 17∘ N
along the coast (Loisel et al., 2017). Upwelling in the VBUS was
confirmed by cruise (Dippner et al., 2007) and remote sensing
observations (Kuo et al., 2000).

Figure 1(a) Model domain and the bathymetry (unit: m) for the
Taiwan Strait Nowcast/Forecast system and carbon, silicon, nitrogen
ecosystem (TFOR-CoSINE) model. Model grid nodes are shown every 25 points. The study area
VBUS is boxed. (b) Zoom-in area of VBUS. Black diamonds are the
observation stations (see text).

In the VBUS, the upwelling intensity is governed by the strength of the
alongshore monsoon wind, as in other coastal upwelling systems such as the
coastal upwelling systems of California and Mid-Atlantic Bight
(Gruber et al., 2011). The VBUS upwelling strength is intense
and can result in surface cooling of 3–5 ∘C and an
associated cold filament length of ∼500 km (Kuo et
al., 2004). The VBUS is modulated by different climatic variations, such as
the El Niño–Southern Oscillation (Dippner et al., 2007; Hein et
al., 2013; Xie et al., 2003), the Indian Ocean dipole (Liu et
al., 2012; Xie et al., 2009), and the Madden–Julian oscillation
(Isoguchi and Kawamura, 2006; Liu et al., 2012).

Previous studies suggested that both the wind-induced upwelling and the
local circulation could influence the nutrient balance and ecosystem in the
VBUS. The El Niño variability is an important controlling factor of the
VBUS by modulating the summer monsoon (Chai et al., 2009; Kuo et al.,
2004). During post-El Niño summer, the weakened southwesterly wind leads
to weak upwelling and reduced upward nutrient flux (Xie et al.,
2003). In addition, Hein et al. (2013) proposed instead that
productivity was controlled by lateral transport of nitrate in the VBUS.
Liu et al. (2002) also highlighted the role of coastal jet located to the
south of the Vietnamese coast. They mentioned that jet-induced upwelling was
responsible for the nutrient influx. Xie et al. (2003) mentioned the
role of the offshore jet and resultant quasi-stationary eddy (i.e., the
“recirculation” hereafter) in transporting the highly productive water.
However, the contribution from recirculation has not been directly
quantified and compared with that directly from the upwelling, which
motivates us to revisit the VBUS ecosystem and its connection with
circulation.

Early hydrodynamic observations revealed a northeastward coastal current
over the southern shelf of Vietnam (Wyrtki, 1961). The current
separates from the coast and flows offshore at about 11∘ N (Xu et
al., 1982). Xie et al. (2003) ascribed the jet separation to the
strong wind jet off Vietnam due to the orographic steering of the
north–south running mountains. Using an idealized reduced gravity model,
Wang et al. (2006) highlighted vorticity input by wind-stress curl
and vorticity advection by the basin circulation. Gan and Qu (2008)
found that the separation was associated with an adverse pressure gradient
induced by the topographic effects.

The separated jet produces cooling and results in biannual sea surface temperature (SST) variation in
the SCS (Xie et al., 2003). The offshore jet also appears to advect
water with high chlorophyll to the interior of the central SCS (Chen et
al., 2014; Loisel et al., 2017; Tang et al., 2004). While the importance of
the local current system to the VBUS biogeochemical system has been noted in
some previous studies (Dippner et al., 2007; Kuo et al., 2004; Liu et
al., 2012; Xie et al., 2003), the detailed processes are unclear. To what
extent is the ecosystem in the VBUS modulated by local circulation? Does the
circulation show any distinction in the high/low production cases? How does
the recirculation modulate productivity? How much does the local circulation
contribute to production? Studying biological production and its coupling
with physical processes in the VBUS will help to answer these questions and
further improve the understanding of boundary upwelling systems. Such a study
will also shed light on the ecosystem dynamics in the SCS as an oligotrophic
marginal sea. Here, we analyze the complex dynamics of the VBUS using a
physical–biological coupled numerical model system, as well as remote
sensing data and in situ observations.

This paper is organized as follows. In Sect. 2, the
model configuration, numerical experiments, observed data, and statistical
method used in this study are described. In Sect. 3, we analyze the remote sensing data and validate
the model. Model results from both the standard run and the sensitivity
experiment are presented. In Sect. 4, the dynamical
processes are analyzed. Conclusions are given in Sect. 5.

2.1 Data

The surface wind vectors were from the Cross-Calibrated Multi-Platform (CCMP)
gridded data. This is a 25-year, 6-hourly, 1/4∘×1/4∘ resolution product fused from several microwave radiometers and
scatterometers using a variational analysis method (Atlas et al., 2011).
Monthly Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua level 3
chlorophyll (4 km resolution) was obtained from the NASA Distributed Active
Archive Center. The estimated monthly vertical-integrated net primary
production (NPP) was derived from MODIS chlorophyll data via the standard
chlorophyll-based Vertically Generalized Production Model (VGPM) algorithm
(Behrenfeld and Falkowski, 1997). The VGPM NPP product had a resolution of
1∕10∘, covering the period from 2004 to the present. Gridded
monthly mean absolute dynamic topography (ADT) with respect to the geoid at
1∕4∘ resolution was acquired. The 1∕4∘ optimum
interpolation sea surface temperature (OISST, also known as Reynolds 0.25v2)
was constructed by combining the Advanced Very High Resolution Radiometer
satellite and other observation data (Banzon et al., 2016). Atmospheric forcing
including downward shortwave radiation, downward longwave radiation, air
temperature, air pressure, precipitation rate, and relative humidity were
acquired from the National Centers for Environmental Prediction reanalysis
(Kalnay et al., 1996). The river runoff data (see Sect. 2.3 below) were
adopted from Dai et al. (2009), which contains observation-based monthly
freshwater runoff of major rivers of the world. The climatology of
temperature, salinity, nutrients, and dissolved organic matter was adopted
from the World Ocean Atlas (WOA, 2013 version). In situ observed nitrate
and chlorophyll profiles from the western SCS stations (Fig. 1b) were used,
as detailed in Jiao et al. (2014).

2.2 Methods

2.2.1 Upwelling intensity (UI) and kinetic energy (KE)

We use the upwelling intensity (UI) or the “Bakun index” (Bakun, 1973) as a
proxy to measure the strength of upwelling (Chen et al., 2012; Gruber et al.,
2011), following the classical paper of Ekman (1905):

(1)UI=τyρ0f=ρaCDUyUyρ0f.

Here, τy is the alongshore component of wind stress, f is the
Coriolis parameter, ρ0 is seawater density (constant, 1025 kg m−3), ρa is the air density (constant, 1.2 kg m−3),
CD is the drag coefficient (constant, 1.3×10-3), and
Uy is the alongshore wind speed. The CCMP data with full temporal and
spatial coverage close to the coastline are used for the wind speed.

The kinetic energy (KE) of the near-surface current is used as an indicator
of the circulation intensity. The near-surface current is calculated from
ADT using the geostrophic balance. The KE then
equals

KE=12ug2+vg2(2)=12ρ02f2∂ADT∂x2+∂ADT∂y2.

Figure 2(a) Summertime (MJJAS) average of surface chlorophyll
concentration (color shading, unit: mg m−3) from MODIS; overlapped
white contours are the mean ADT, with the arrows showing the directions of surface
currents. The gray box is the region of interest (VBUS), while AE shows the
center of the anticyclone. (b) Standard deviation of surface
chlorophyll (color shading, unit: mg m−3) overlapped with the contours
of surface KE with an interval of 0.1 from 0.1 to 1.0 (unit:
m2 s−2). The magenta dot–dash contour delimits the ocean region
over which chlorophyll and KE are averaged (see text).

2.2.2 Multivariable linear regression

Monthly NPP from VGPM (see Sect. 2.1) was used to estimate biological
productivity. A multivariable linear regression analysis was conducted to
examine the statistical relations among NPP, UI, and KE:

(3)NPP=b1UI+b2KE+b3,

where b1, b2, and b3 are the estimated parameters of the
regression. Data in the summer months (MJJAS) were used since the monsoon
wind during this period is upwelling favorable. The focus region of this
study is not confined to the “actual” upwelling strip of ∼40 km width as indicated by the first baroclinic Rossby radius of deformation
(Dippner et al., 2007; Voss et al., 2006) but extended to a
broader offshore region of ∼3∘ width. Therefore, we
averaged NPP and KE over the ocean region enclosed by the magenta contour
off the coast of Vietnam (Fig. 2b). Only the
summertime data in the overlapping period from 2004 to 2012 were analyzed.
Contributions from SST, day length, and the photosynthetically active
radiation were implicitly considered in the VGPM (Behrenfeld and
Falkowski, 1997).

2.3 Model description

We use a three-dimensional general circulation model based on the Regional
Ocean Model System (ROMS). ROMS is a free-surface and hydrostatic ocean
model. It solves the Reynolds-averaged Navier–Stokes equations on
terrain-following coordinates (Shchepetkin and McWilliams,
2005). The model is used in the operational Taiwan Strait Nowcast/Forecast system (TFOR), which successfully provides multi-purpose ocean
forecasts (Jiang et al., 2011; Liao et al., 2013; Lin et al., 2016; Lu et
al., 2017, 2015; Wang et al., 2013). In this study, the model
grid is modified to cover the whole SCS domain and part of the northwestern
Pacific with a grid resolution of 1∕10∘ (Fig. 1a). The number of grid nodes in x and y directions is 382 and 500,
respectively. In the vertical, 25σ levels is used with a grid size
of ∼2 m on average near the surface to resolve the surface
boundary layer. Following the bulk formulation scheme (Liu et al.,
1979), daily atmospheric fluxes (detailed in Sect. 2.1) are applied at the
surface. The wind vectors are from the CCMP wind. The vertical turbulent
mixing uses a K-profile parameterization (KPP) scheme (Large et al.,
1994) which was successfully applied in a one-dimensional vertical mixing
model in the SCS (Lu et al., 2017). The KPP scheme estimates eddy
viscosity within the boundary layer as the production of the boundary layer
depth, a turbulent velocity scale, and a dimensionless third-order
polynomial shape function. Beyond the surface boundary layer, the KPP scheme
includes vertical mixing collectively contributed by shear mixing, double
diffusive process, and internal waves. Biharmonic horizontal mixing scheme
(Griffies and Hallberg, 2000) with a reference viscosity of
2.7×1010 m4 s−1 is applied, following the value of
Bryan et al. (2007) used in a circulation model with the same
horizontal resolution. Climatological river discharge from the Mekong River
and other major rivers is included as point sources.

The biogeochemical module is the carbon, silicon, nitrogen ecosystem (CoSINE)
model (Xiu and Chai, 2014), which consists of 31 state variables, including
four nutrients – nitrate (NO3), ammonium (NH4), silicate,
and phosphate, three phytoplankton functional groups (representing
picoplankton, diatoms, and coccolithophorids), two zooplankton classes (i.e.,
microzooplankton and mesozooplankton), four detritus pools (particulate
organic nitrogen/carbon, particulate inorganic carbon, and biogenic silica),
four kinds of dissolved organic matter (labile and semi-labile pools for both carbon
and nitrate), and bacteria. Other planktonic groups can be important in the
ecosystem of SCS in some condition (Bombar et al., 2011; Doan-Nhu et al.,
2010; Loick-Wilde et al., 2017). Surely, adding more planktonic groups would
better depict the complex relationship in the ecosystem. However, considering
the functional groups chosen here were dominating species widely observed in
the SCS (e.g., Ning et al., 2004), adding more planktonic species is unlikely
to radically change the spatiotemporal variation of the modeled ecosystem. To
keep the ecosystem model simple and computationally affordable, these groups
are not considered in the CoSINE model. The CoSINE model was well applied in
various studies of the SCS. Liu and Chai (2009) investigated the seasonal and
interannual variability of the primary productivity of the SCS at a basin
scale. The modeled structure (e.g., phytoplankton community) and function
(e.g., biological pump) of the ecosystem appeared to respond to both climatic
variations (Ma et al., 2013, 2014) and mesoscale eddies (Guo et al., 2015),
both well captured by CoSINE. By taking a SCS average, the modeled NPP
time series showed a strong correlation (R=0.84) when compared with
satellite-derived production (Ma et al., 2014). Details of the CoSINE results
in SCS can also be found in Lu et al. (2018).

The physical model was initialized from a resting state with temperature
and salinity specified using the WOA climatology. The initial distribution
of the nutrients and dissolved organic matter was also interpolated from the
WOA climatological data. Our previous studies related with ecosystem
modeling in the Chinese seas (Lu et al., 2015; Wang et al., 2016, 2013) suggested that the ecosystem module was more sensitive to the
initial value of nutrients and dissolved organic matter. For other
variables (i.e., detritus and plankton), the model converged to similar
states even when these variables were initialized differently. Hence, for
these ecosystem variables, small values were assigned as in Table S1 in the Supplement. After
spinning up for 13 years with climatological forcing, the model was
restarted with the ecosystem module driven by interannually varying CCMP
wind and atmospheric surface forcing from 2002 to 2011. The model output
from 2005 to 2011 is analyzed.

2.4 Sensitivity experiment

To quantify the contribution to the ecosystem from the recirculation, we
seek to control the formation of the recirculation while maintaining the
larger basin-scale circulation. For the Vietnam boundary upwelling system,
since nonlinear advection is important to the separation of the coastal jet
and thus the formation of the anticyclone (Gan and Qu, 2008;
Wang et al., 2006), which is familiar in the Gulf Stream separation problem
(Haidvogel et al., 1992; Marshall and Tansley, 2001), an experiment
without the nonlinear advection terms in the momentum equations was
conducted (following, e.g., Gruber et al., 2011). It should be
noted that the advection terms in the tracer equations are retained for
transport of active and passive tracers (i.e., ecosystem variables).
Hereafter, this experiment will be referred to as the NO_ADV run.

In this section, we first analyze the satellite-based observational data,
focusing on the spatiotemporal covariance of wind, circulation, and
biological production. After accessing the model performance against
observation, we then describe and discuss the model results.

3.1 Spatiotemporal analysis of observation data

Figure 2 shows the mean
(Fig. 2a) and standard deviation
(Fig. 2b) of surface chlorophyll overlaid with
contours of mean ADT and KE, respectively. In summer, the surface chlorophyll
has a low concentration of <0.1 mg m−3 in the central SCS
basin. By contrast, the chlorophyll is more than 5-fold (>0.5 mg m−3) along the southern Vietnamese coast. The high chlorophyll water
appears to extend offshore following the coastal jet to the interior SCS.
The jet overshoots after separating from the coast and bifurcates into a
northeastward current and a quasi-stationary anticyclonic eddy
(Fig. 2a). Centered at ∼11∘ N near the tip of the Vietnamese coast, high KE (>1.0 m2 s−2) appears near the coast. The high variability of
chlorophyll coincides with KE into the interior SCS, implying the
contribution from the jet (Fig. 2b).

The box-averaged (magenta box in Fig. 2b) time series of monthly UI, KE, and
NPP are shown in Fig. 3a–c; they show seasonal and
interannual variations. KE and NPP both present biannually signals in most
years, i.e., peaks in summer and winter, as well as complex non-seasonal
signals. Unsurprisingly, UI dominates about half (R2=0.4548 for UI
solely, p<0.01) of the total variability in NPP, which is
consistent with studies in other wind-driven upwelling systems (Gruber et al., 2011), and with the previous studies of the VBUS (Bombar et al., 2010; Voss et al., 2006). The correlation between KE and
NPP is even higher (R2=0.4930, p<0.01). Moreover, although
KE could be dependent on the wind, the R2 between KE and UI is 0.3240,
suggesting that a large part (∼68 %) of the variation in KE
is unexplained by the uniform alongshore wind. There are clear positive
contributions to the biological production from both UI and KE. When KE and
UI are considered concurrently, an additional ∼15 % of the
variability in NPP is explained (R2=0.6046, p<0.01).

To further illustrate the modulation in the ecosystem by circulation, the
flow patterns in high NPP anomaly (HNA) and low NPP anomaly (LNA) were
composited according to the de-seasonalized NPP anomaly (Fig. 3c). The
seasonal signal was firstly removed from the summertime NPP, yielding the
de-seasonalized NPP anomaly. The thresholds for HNA and LNA are defined as
(above) 75 % and (below) 25 % percentile of the NPP anomaly,
respectively. Different thresholds (60 % and 70 %) were also tested
and very similar results can be seen. The velocity and direction for LNA,
HNA, and the normal state (i.e., neither LNA nor HNA) are respectively depicted in
Fig. 4a–c, as well as the ADT difference between HNA and LNA (Fig. 4d). A
Student's t test suggests that the three circulation patterns are
significantly (p<0.01) different. In contrast to the familiar separation
and offshore jet pattern (Figs. 2a and 4c), the LNA circulation tends to flow
along the coast without separation (Fig. 4a). On the other hand, the HNA
circulation (Fig. 4b) shows a clear separated jet and anticyclonic
recirculating pattern south of the jet near 8.5∘ N, similar to the
pattern seen during normal years (Fig. 4c); the flow speed is ∼20 %
stronger than that of the normal state. Near the separation point, the HNA
jet is more dissipated and slightly weakened compared with the LNA coastal
jet. The KE averaged within the magenta box (Fig. 2b) during the HNA state is
0.0827 m2 s−2, which is ∼65 % larger than that during
the LNA state (0.0502 m2 s−2). The difference in flow patterns is
consistent with a dipolar ADT difference, by which a westward (inverse to the
jet) pressure gradient force anomaly is imparted to the flow (Fig. 4d), which
is responsible for the jet separation process (Batchelor, 1967; Gan and Qu,
2008). In order to examine the recirculation's role in the ecosystem, we now
use the model to address the physical–biogeochemical coupling.

3.2 Model validation

In Fig. 5, simulated SST and NPP are compared with observations. The model
reproduces reasonably well the observed patterns of SST and NPP. In
particular, the model captures the cross-shore SST gradient. The cold
filament that overshoots from the coast to the interior of SCS is also
clearly reproduced by the model. However, the modeled SST shows a systematic
cold bias of ∼ 1 ∘C, and the modeled NPP does not simulate
well the extremely high values (>1000 mg C m−2 day−1) along
the Vietnamese coast. This may in part be attributed to overestimation of
retrieved NPP near the coast (Loisel et al., 2017). Off the coast, the model
simulates well the cross-shore gradient of productivity. The gradient is
generally high in areas influenced by the jet. In the coupled model, while it
is true that SST affects NPP through, for example, changes in the vertical
stratification of the water column, both SST and NPP strongly depend on
circulation (e.g., upwelling and/or downwelling), and in our case on the flow
separation and KE also. In turn, the circulation is dominated by changes in
the upper-layer depth (as diagnosed through the SSH) and the horizontal
gradients of SSH, and is much less dependent on the gradients of SST. Thus,
the covariation between the SST and ecosystem is largely controlled by the
circulation. The dominant ecosystem response is the separation and
non-separation contrast, which is captured well by the model (comparing
Figs. 4 and 8).

Time series of modeled SST, surface KE, and NPP, averaged over the magenta
contour region (Fig. 2b), are compared with observations in
Fig. 6. Due in part to the realistic surface
forcing and high resolution used, our model can reproduce the physical and
biological variables in the VBUS. The seasonal cycles in all three
quantities agree reasonably well with the observations. At interannual
timescales, during the 2010 El Niño event, for example, the monsoon was weaker
(Fig. 3a), SST was warmer, and the KE was reduced.
These features are simulated well, although the production drawdown is
slightly weaker than the observation and the simulated SST underestimates
amplitude of the observed SST annual cycle by ∼1.0∘C. For the surface current and productivity, our model shows
excessive KE and insufficient production during winter, but the
model–observation discrepancy is less notable in other seasons. The
overestimated KE is partially contributed by the Ekman component in the
modeled surface current. Nevertheless, we can conclude that our model
reasonably reproduced the temporal variability in the VBUS.

Figure 7The vertical profiles of (a) nitrate concentration (unit:
mmol m−3) and (b) chlorophyll concentration (unit:
mg m−3). In both plots, the black dots are the observation values (see
Fig. 1b for stations). The gray area shows the envelope for all model stations
in the same area and month, while the red lines are the area-mean profiles.

In addition, vertical profiles of the simulated NO3 and chlorophyll, as
two fundamental components of the marine ecosystem, are compared with
observations (Fig. 7). The modeled NO3
generally reflects the oligotrophic condition near the surface and the
nutricline approximately at 50 m. Below the nutricline, the NO3 profile shows moderate vertical gradient to the deep. The simulated
NO3 profile matches the observations remarkably well. For the
chlorophyll, our model well simulates the concentration, not only at the
surface but also in the deep layer. A subsurface chlorophyll maximum appears
at ∼35 m, which is somewhat shallower than that in the
observation (50 m). Except for model uncertainty, this discrepancy may also
be related to the undersampling in observed profiles (no water samples
between 25 and 50 m depth). When chlorophyll is considered as a proxy of
NPP, vertically integrated chlorophyll is more relevant. The
vertical-averaged (5 to 150 m) chlorophyll concentrations in the model and observation
are 0.1595 and 0.1668 mg m−3, respectively, which have a marginal
difference (<5 %). Both the modeled and observed chlorophyll
concentrations have a large range from 0 to >1.0 mg m−3 in the subsurface chlorophyll maxima. This reflects the large spatial
variability in chlorophyll.

Following the analysis in Sect. 3.1, the
multi-variable regression analysis on the model output was also conducted.
The modeled NPP presents a phase lag with respect to the UI and KE
variation. When NPP is lagged for 1 month, the correlation is 0.752 with a
p value of 0.0214, suggesting a significant regulation of the physical
forcing to the productivity. Additionally, the composites of the HNA, LNA,
and normal scenarios (Fig. 6c) based on model output show contrasts among
scenarios comparable to those in the observed cases in
Fig. 4, further suggesting the reasonability of the
model simulation (Fig. 8).

In summary, one could find that our model performs reasonably in reproducing
the key spatiotemporal features in the hydrodynamics and ecosystem of VBUS.
Inevitably, some discrepancies exist, which are less evident in the summer
months. Nevertheless, considering the focus is to investigate the positive
correlation between the productivity and the circulation, which was captured
by the model (Fig. 8), these shortcomings could be accepted.

3.3 Analysis of model results

Modeled circulation and potential density from the multi-summer average are
presented in Fig. 9a–d, with sea surface height
overlaid. In the upwelling region, the doming of isopycnals (Fig. S2) is
discernable as in the classical coastal upwelling models (O'Brien and
Hurlburt, 1972). Consistent with previous studies, the coastal current flows
northward along the shelf (Hein et al., 2013). The current also
disperses freshwater from the Mekong River, while the water seldom spreads
away from the coast. The coastal current veers at ∼11∘ N, directs offshore, and then separates, forming the
quasi-stationary anticyclone centered at ∼ 9∘ N, 110∘ E. Near the core of the anticyclone, vigorous vertical motion
near the surface can be found, implying submesoscale processes in play. Near
108∘ E, intensive onshore flow ascends on the slope. The
high-density bottom water outcrops at 107∘ E, rejoining the
coastal water and directing north, thus forming a circuit.

The biogeochemical variables reveal that the ecosystem is largely controlled
by the circulation (Figs. 9e–h, S2). Lateral
nutrient gradient appears at the periphery of the anticyclone, which is
characterized with depressed nitrate isosurface in the core and domed
isosurface due to the upwelling and river injection near the coast
(Fig. 9e). Stimulated by the river-injected and
locally upwelled nutrients near the coast, primary production (PP) shows a
surface maximum of >30 mg C m−3 day−1 (Fig. 9g). The water with high production is then
advected offshore by the jet (Fig. 9h), leading to
an offshore bloom patch in a curved shape which is familiar along the
Vietnamese coast (e.g., Fig. 5c). The jet also conveys the
water with high particulate organic carbon offshore. The distribution of
particulate organic carbon is somewhat deeper and more spread than that of
high PP water, suggesting the vertical sinking and lateral transport
processes (Nagai et al., 2015). Remineralization of organic carbon
results in a subsurface ammonium maximum at ∼50 m
(Fig. 9f) consistent with other studies in SCS (Li et al., 2015). Part of the ammonium could then fuel
nitrification and production, while the rest rejoins the circulation with
the upwelling water in the bottom Ekman layer. In summary, the model output
clearly reveals circuiting circulation and a cycled ecosystem, which will be
further discussed in Sect. 4.

4.1 Biogeochemical cycle in the VBUS

In the summer VBUS system, it is generally agreed that the wind's
predominant role is controlling the variability in the production of VBUS,
especially on the interannual scale (Dippner et al., 2013). This is
also the case in our analysis, where UI contributes ∼45 % of
the total variability in production. In addition, via analysis of satellite
data and model output, consistent and robust positive contribution from the
local circulation to the biological production was also revealed. The
contribution of the circulation is distinct from the major coastal upwelling
systems, where the offshore transport by the mean current appears to
suppress the production by reducing the nearshore nutrient inventory
(Gruber et al., 2011; Nagai et al., 2015). The separated current system
was considered to transport high-chlorophyll water offshore (Xie et
al., 2003). In the offshore region, the production appeared to be elevated
(Bombar et al., 2010). However, the fate of the offshore nutrients
was rarely investigated in the literature.

Comparing the ecosystems in LNA and HNA (Fig. 10),
the following stages of the cycle can be deduced: (1) the upwelled and
riverine input nutrients (majorly inorganic) stimulate high production near
the Vietnamese coast (Dippner et al., 2007); (2) the produced organic
matter is transported offshore by the jet; the water has high chlorophyll
(e.g., Fig. 2a) and high organic matter (Fig. 9h)
in the euphotic zone; (3) a significant portion of the nutrients (majorly in
organic form) is transported back to the south of VBUS by the westward
recirculation. The quasi-stationary rotating anticyclone impedes further
offshore leakage of the nutrients (Fig. 10i). (4) The trapped organic matter is remineralized, forming the subsurface maxima
of ammonium (Fig. 9f, which supports regeneration production with an
f ratio of ∼60 %, Table 1) and replenishing the nitrate by
nitrification. The offshore remineralization can be supported by the high
oxygen consumption found off Vietnamese coasts (Jiao et al.,
2014). Afterward, the nutrients are upwelled by bottom Ekman pumping (see
high amount of nutrients in the bottom boundary layer in Fig. 9e) and wind-induced
upwelling, and finally rejoin in the local biogeochemical cycle. The
recirculation may determine the available nutrient inventory, therefore
playing a significant role in controlling the productivity, which will be
further supported by the experiment in the following section.

4.2 Dynamic analysis

By controlling the available nutrients, the recirculation modulates the
productivity in the VBUS. The influence of the recirculation is further
elucidated below. Table 1 summarizes the difference of the ecosystems in the
standard run and NO_ADV experiment. The NO_ADV
experiment can be regarded as an extreme case where the circulation shows
very weak tendency of separation without the recirculation (also see Fig. S1),
which is representative of the LNA scenario. In terms of force
balance, the recirculation is maintained by the balance between the inertial
force and the Coriolis force. Without the nonlinear term, this balance could
not exist. The horizontal and vertical fluxes of nitrate in three scenarios
are also depicted in Fig. 11.

In the VBUS, similar to other coastal upwelling systems
(Steemann-Nielsen and Jensen, 1957), the availability of nutrients
principally controls the productivity. Considering a quasi-steady state of
nutrients in a coastal region, river-injected and upward inputted nutrients
should be counter-balanced by vertical export production and lateral
exchanges. The lateral exchanges include both advection and diffusion, while
it was pointed out that horizontal mixing is 1 or 2 orders of magnitude
lower than that of horizontal advection (Lu et al., 2015). Hence,
as a sink term, the lateral exchanges are determined mostly by the advective
fluxes normal to the boundary of the predefined box. The diagnostic also
suggests a dominance role of advection process in the vertical overmixing.
Given the fact the standard run and NO_ADV experiment have
the same riverine input and similar export flux, one could infer that the
difference between two model cases is largely due to the different lateral
transports and upwelled fluxes of nutrients.

In the LNA (Figs. 4a and 8a), and also in the NO_ADV experiment (Fig. S1),
the circulation pattern switches to the along-isobath pattern, which modifies
the local biogeochemical cycle. More nutrients are transported northward and
offshore out of VBUS and never come back, leading to a reduction of nutrients
(Fig. 11). This effect can be demonstrated by cross-section nutrient flux
across the 109∘ E section. The more nutrient leakage, the less westward
nutrient flux across this section. In the NO_ADV run, the westward flux of
nitrate is significantly reduced by 36.2 % (Table 1). The reduction of
nutrients is accompanied by suppressed the upward nutrient flux
(−46.5 %) near the shelf edge (∼100 m). As a consequence of more
leakage and less upwelling influx, the nitrate reservoir and new production
are significantly reduced by 20.7 % and 21.9 %, significantly
inhibiting the primary production process (15.7 %, Table 1). Other
ecosystem constituents decrease to a limit degree, such as −2.6 % for
ammonium and −3.0 % for dissolved organic carbon (DOC). This interpretation is further supported
by the post-El Niño scenario. In 2010, more significant suppression
occurs in the vertical nutrient flux (−99.6 %), while the horizontal
fluxes also respond to decrease. Due to the drawdown in the wind-induced
upwelling and recirculation (Table 1), the production is extremely low in
summer 2010 (Fig. 3c).

The larger the KE, the more intensive the separation (see Appendix B for additional
discussion about this point). This separation was similar to the Gulf Stream
detachment problem in many aspects. In particular, various factors could
affect the intensity and position of the separation (Chassignet and
Marshall, 2008). Generally, the mechanism of the current separation was
considered to be ascribed to the wind stress curl (Xie et al., 2003).
We also found that the current separation was very sensitive to the wind
stress curl (not shown in figures). Hence, one may argue that this
covariability is controlled by the variation of wind forcing. However, as
mentioned in Sect. 3.1, a large part (∼68 %) of the
variation in KE is unexplained by the large-scale wind. In addition, our
sensitivity experiments without the nonlinear advection of momentum showed
very weak separation, in agreement with that of Wang et al. (2006).
This implies wind forcing is not the only factor in controlling the current
system, while the intrinsic dynamics are also important. The accelerated
coastal current is also associated with intensified cross-isobath transport
by bottom Ekman effect (Gan et al., 2009) or dynamic upwelling
(Yoshida and Mao, 1957) due to the rotational current (Dippner
et al., 2007). Hence, low KE reduces the recirculation of nutrients.
Combining all the effects, the intensified current is a condition favorable
for the nutrient inventory and hence the productivity. To a larger scale,
the recirculation current couples coastal upwelling and the offshore region in
the major coastal upwelling systems, e.g., in the Canary basin (Pelegrí et al., 2005). In this study, this
effect was also found to be important, which may contribute up to 15 % of
the productivity variability.

Figure 12Schematic diagram summarizing the dynamics in different scenarios of
distinct circulation pattern in the VBUS, overlapped with the
three-dimensional distribution of PP (unit: mg C m−3 day−1).
(a) Normal state: the separated jet transports the upwelled
nutrients and produced organic matter offshore. While a substantial portion of the
offshore transported organic matter leaks into the interior of SCS and never
comes back, the recirculation and quasi-stationary anticyclonic eddy trap the
organic matter locally and hinder further leakage of available nutrients in
the VBUS. The locally recirculated nutrients are then upwelled in the bottom Ekman
layer, rejoining the production process over the shelf.
(b) Non-separation state: during the non-separated circulation, the
along-isobath circulation transports the organic matter northward. The
leakage of organic matter reduces the nutrient inventory in the VBUS. The
loss of nutrients diminishes the nutrient inventory available for
remineralization and upwelling, further inducing a reduction in the
production process.

Via analyzing the summertime remote sensing data in the VBUS, a tight
spatiotemporal covariation between the ecosystem and near-surface circulation
was revealed. The water with high kinetic energy appeared to coincide with
high chlorophyll variability. Statistical analysis suggested that the high
level of productivity was associated with the high level of circulation
intensity, which accounted for ∼ 15 % of the variability in
productivity. Elevated kinetic energy and intensified circulation were
related to the separation of the upwelling current system. Especially, in
the low-productivity scenarios, the circulation pattern shifted from the
intensive separation pattern to a moderate alongshore non-separated pattern.

A physical–biological coupled model was applied to investigate the positive
contribution from the circulation intensity to the productivity. A numerical
experiment was also designed to reproduce the weak-separated circulation
pattern without the recirculation. Inspection into the model results
highlighted the recirculation's role in the local biogeochemical cycle. As
presented in the schematic diagram in Fig. 12, the
separated circulation and resultant recirculation were favorable for the
nutrient inventory. During non-separation scenarios, the nutrients
transported northward by the alongshore current would never come back, leading to a
nutrient leakage. The nutrient leakage further induced the feedback
summarized in Fig. 12b, which could reduce the
nitrate inventory by ∼21 % and the NPP by ∼16 % in the experiment representative for very weak separation. The
weakened coastal current was also associated with reduced dynamic upwelling,
hence further reducing the vertical flux of nutrients. This resulted in the
positive contribution to the productivity.

This finding provides new insight into the complex physical–biological
coupling in the Vietnam coastal upwelling system. Moreover, this
understanding could help to predict the future reaction of productivity in
the SCS. As revealed by Yang and Wu (2012), the summertime
near-surface circulation of SCS had experienced a long-term trend of being
more energetic, characterized with intensified separation and recirculation
in the VBUS (see their Fig. 9). Whether this long-term trend of
circulation will also induce a potential trend in the ecosystem in response
to future climate changes is a topic of common interest, which merits
further investigation.

Qualitatively, both the analyses based on remote sensing data and model
results suggest the separation flow is linked with stronger KE (∼65 % larger in the HNA case than in the LNA case; Sect. 3.1). Moreover, a
separation index (SI) is defined to quantitatively explain the relation
between the flow separation and intensified circulation. The SI can be
written as

(B1)SI=∑u⋅cosφ+v⋅sinφu2+v2,

where u and v are the two surface velocity components, and φ is the
angle between the topography gradient and the positive x axis. This SI is
essentially the area-averaged cross-isobath velocity normalized by the
magnitude of the velocity, which is used to quantify flow separation here.

Figure B1 shows the spatial distribution of SI in August 2010. The positive
values indicate the flow is downslope, while negatives suggest ascent. Large
SI can be observed near the separation point (∼ 11.5∘ N). Taking the spatial average over the box region in Fig. B1, a generally good
positive correlation (R=0.7175, p<0.01) between log(KE) and SI is
found (see Fig. B2). The log(KE) and SI presents a logistic-type
relationship, where SI asymptotically approaches a maximum value of
∼0.35. This suggests that the strong flow separation and
elevated KE are tightly linked. Further, from the scatter plot of KE vs. SI
(Fig. B2), we find that 0.1 m2s−2 is a critical value, which
divides the data into two subsets while minimizing the slope of the right
part (blue fitting curve in Fig. B2).

Figure B1 shows the distribution of SI in August 2010. Positive values indicate
that the flow is separating and downslope, and that may be seen off Vietnam
south of the coastline bend. Large SI (∼1.0) can be observed
near the separation point ∼11.5∘ N. Taking the spatial
average over the box region in Figs. 2a or B1, there is a good positive
correlation (R=0.7175, p<0.01) between log(KE) and SI (see Fig. B2).
Moreover, SI may be seen to generally increase with KE to a value of
0.25–0.3 and then level off (i.e., the slope becomes less);
see the red and blue lines in Fig. B2. The log(KE) and SI thus appear to
show a logistic-type behavior, in which SI asymptotically approaches some
maximum value (in this case ∼0.3). This suggests that the
strong flow separation and elevated KE are tightly linked. From Fig. B2, the
value of KE ≈0.1 m2s−2 appears to be a critical value.

Dynamically, the nonlinear advection term in the momentum equation can be
written as the vector-invariant form (see, e.g., Gill, 1982):

(B2)u→⋅∇u→=(∇×u→)×u→+∇12u→2.

This decomposition directly links the nonlinear advection term and the
gradient of KE (which scales KE over a length scale L). Meanwhile, the
nonlinear advection is an important mechanism in driving flow separation
(see, for instance, Oey et al., 2014). Stronger advection suggests
intense cross-isobath flow. Therefore, a dynamic linkage between the flow
separation and the intensified KE and circulation can also be established,
further supporting this argument.

WL, EL, and YJ conceived the research, conducted the
numerical calculation, and drafted the manuscript. LYO interpreted the
results, designed the experiments, and revised the manuscript. WZ contributed to discussing and analyzing the
results. XHY contributed to improving the manuscript. All co-authors
contributed to integrating the study, and writing and refining the
manuscript.

This study was supported by grant no. 2016YFA0601201 from the Ministry of
Science and Technology of the People's Republic of China (MOST), and grants
41476005, 41476007, 41876004, and 41630963 from the National Natural Science
Foundation of China (NSFC). This study was also partially supported by the Fujian
Collaborative Innovation Center for Big Data Applications in Governments (2015750401)
and the Central Guide Local Science and Technology Development Projects (2017L3012).
The authors would like to thank Fei Chai for
providing the code of the CoSINE model. The authors would also like to thank the
three reviewers for their useful comments.

Xu, X., Qiu, Z., and Chen, H.: The general descriptions of the horizontal
circulation in the South China Sea, in: Proceedings of the Symposium of the
Chinese Society of Marine Hydrology and Meteorology, Chinese Society of
Oceanology and Limno, Science Press, Beijing, China, 137–145, 1982.

In this study, we investigate the physical factors controlling the biological production in a coastal upwelling system, the Vietnam boundary upwelling system in the South China Sea. We found that, in addition to the effects from the wind (as a major factor driving the ocean), the ocean circulation could also contribute positively to the production here, which is distinct from other major coastal upwelling systems.

In this study, we investigate the physical factors controlling the biological production in a...